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20242026
most citedSignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning

5 citations · 7 across the 13 of their papers we have counts for

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11 papers · 1 filter

cs.CV2026

Optimize Surgical Triplet Recognition: A Knowledge-Driven Mixture-of-Experts Solution

Yiyi Zhang, Yuchen Yuan, Ying Zheng +4

Surgical action triplet recognition constitutes a critical task in context-aware robot-assisted surgery, facilitating automatic surgical action perception by identifying instrument…

cs.CV2026

Adapting 2D Multi-Modal Large Language Model for 3D CT Image Analysis

Yang Yu, Dunyuan Xu, Yaoqian Li +3

3D medical image analysis is of great importance in disease diagnosis and treatment. Recently, multimodal large language models (MLLMs) have exhibited robust perceptual capacity, s…

cs.CV2026

Med-Evo: Test-time Self-evolution for Medical Multimodal Large Language Models

Dunyuan Xu, Xikai Yang, Juzheng Miao +3

Medical Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across diverse healthcare tasks. However, current post-training strategies, such as super…

cs.CV2025

Perceive and Calibrate: Analyzing and Enhancing Robustness of Medical Multi-Modal Large Language Models

Dunyuan XU, Xikai Yang, Yaoqian Li +3

Medical Multi-modal Large Language Models (MLLMs) have shown promising clinical performance. However, their sensitivity to real-world input perturbations, such as imaging artifacts…

cs.CV2025

MEJO: MLLM-Engaged Surgical Triplet Recognition via Inter- and Intra-Task Joint Optimization

Yiyi Zhang, Yuchen Yuan, Ying Zheng +4

Surgical triplet recognition, which involves identifying instrument, verb, target, and their combinations, is a complex surgical scene understanding challenge plagued by long-taile…

cs.CV2025

From Learning to Unlearning: Biomedical Security Protection in Multimodal Large Language Models

Dunyuan Xu, Xikai Yang, Yaoqian Li +2

The security of biomedical Multimodal Large Language Models (MLLMs) has attracted increasing attention. However, training samples easily contain private information and incorrect k…